Claude 3.5 Sonnet is the strongest general-purpose LLM for coding and document analysis as of May 2026. GPT-4o remains the better choice for multimodal tasks (images, audio) and applications that depend on broad third-party integrations. Neither model is universally superior — the right answer depends on your workload.
What Claude 3.5 Sonnet Does Better
Longer Context Window
Claude 3.5 Sonnet supports 200,000 tokens in a single context window. GPT-4o supports 128,000 tokens. The difference is substantial when your workflow involves long documents — legal contracts, research papers, large codebases, full meeting transcripts. With Claude, you can load an entire 150,000-word novel and ask questions about it. With GPT-4o you are often forced to chunk the document and lose cross-chunk coherence.
The context window difference also matters for coding sessions where you want to keep a large codebase in memory. Claude's extra 72,000 tokens can be the difference between fitting an entire feature's related files in context or having to make strategic tradeoffs about what to include.
Coding Performance
SWE-Bench Verified is the most meaningful coding benchmark because it tests performance on real GitHub issues, not textbook problems. Claude 3.5 Sonnet scores approximately 49% on SWE-Bench Verified. GPT-4o scores approximately 38%. That is a meaningful gap in real-world software engineering capability.
On HumanEval, which tests Python coding problems, Claude 3.5 Sonnet scores 92% and GPT-4o scores 90.2%. The gap is smaller here, but Claude maintains the lead.
In practice, developers using Claude for code generation report fewer hallucinated APIs, better handling of edge cases, and more accurate implementation of complex algorithms. Claude is particularly strong at understanding what code is supposed to do from context and maintaining consistency across a long implementation.
Instruction Following on Complex Tasks
Claude 3.5 Sonnet follows complex, multi-step instructions more reliably than GPT-4o. If you write a detailed system prompt with specific formatting requirements, constraints, and output structure, Claude is more likely to honor all of it simultaneously. GPT-4o tends to drift from complex instruction sets, especially when the task requires simultaneously maintaining multiple constraints across a long output.
This matters for production applications where your system prompt defines important behavior. An AI writing assistant that ignores your tone guidelines halfway through, or a structured data extractor that occasionally deviates from your output format, creates downstream problems that are expensive to debug.
Document Analysis
For tasks like summarizing long reports, extracting structured information from unstructured documents, or comparing multiple documents against each other, Claude 3.5 Sonnet's larger context window and strong instruction following combine to make it the better tool. You can load more of the source material at once and ask Claude to apply precise extraction rules to all of it.
What GPT-4o Does Better
Multimodal Capabilities
GPT-4o was built with true multimodal architecture from the ground up. It handles images, audio, and text natively. You can send it a photograph and ask complex questions, have it describe what it sees in detail, or use it to transcribe and analyze audio recordings.
Claude 3.5 Sonnet supports image input but is weaker at complex image reasoning tasks, particularly tasks that require understanding spatial relationships, interpreting charts and graphs, or analyzing visual ambiguity. If your application involves significant image or audio processing, GPT-4o is the stronger choice.
Tool Use Reliability
GPT-4o is more reliable at using tools (function calling) correctly on the first attempt. When you define a set of tools and ask the model to use them to accomplish a goal, GPT-4o is more likely to select the right tool, call it with correct parameters, and chain multiple tool calls coherently. Claude 3.5 Sonnet is capable of tool use but requires more careful prompt engineering to get consistent results.
Third-Party Integrations
GPT-4o is integrated into far more third-party products. Microsoft Copilot, GitHub Copilot, and hundreds of SaaS tools use GPT-4o under the hood. If you are building on top of an existing platform that uses OpenAI, or if your team uses tools that connect to OpenAI's API, GPT-4o may be the practical choice regardless of benchmark differences.
Pricing
GPT-4o is less expensive. Input tokens cost $2.50 per million for GPT-4o versus $3 per million for Claude 3.5 Sonnet. Output tokens cost $10 per million for GPT-4o versus $15 per million for Claude 3.5 Sonnet. At high volume, this difference compounds quickly. A system generating one billion output tokens per month saves $5,000 per month using GPT-4o over Claude Sonnet.
Benchmark Summary
| Benchmark | Claude 3.5 Sonnet | GPT-4o | |---|---|---| | MMLU | 89.0% | 88.7% | | HumanEval | 92% | 90.2% | | SWE-Bench Verified | ~49% | ~38% |
MMLU measures broad knowledge across subjects. Both models are neck-and-neck. The real differentiation shows in coding benchmarks.
Pricing Summary
| | Input | Output | |---|---|---| | Claude 3.5 Sonnet | $3 / 1M tokens | $15 / 1M tokens | | GPT-4o | $2.50 / 1M tokens | $10 / 1M tokens |
Note: Anthropic offers prompt caching that gives a 90% discount on cached input tokens, which can significantly change the effective cost for applications with stable system prompts.
How to Choose
Use Claude 3.5 Sonnet when:
- Your workload is primarily coding and software engineering
- You need to process long documents (contracts, codebases, research)
- You have a complex system prompt that requires strict adherence
- You want the best SWE-Bench performance available
Use GPT-4o when:
- Your application processes images or audio
- You need reliable tool use with minimal prompt engineering
- You are building on platforms already integrated with OpenAI
- You are price-sensitive and the benchmark gap is acceptable for your use case
Both models are excellent. The difference is specialization, not a clear winner.
Keep Reading
- How Large Language Models Work — understand what is happening inside both models
- LLM Comparison Guide 2026 — broader comparison across all major models
- Best LLM for Coding 2026 — focused analysis on which models produce the best code
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